



'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase_  :							Tuple             =    logging.get_logger(__name__)


lowerCAmelCase_  :							Optional[int]             =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase_  :							Any             =    {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_  :							Dict             =    {
    'RUCAIBox/mvp': 1024,
}
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Union[str, Any]						  =					VOCAB_FILES_NAMES
						_lowerCAmelCase  : List[Any]						  =					PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase  : str						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase  : List[Any]						  =					['input_ids', 'attention_mask']
						_lowerCAmelCase  : List[Any]						  =					MvpTokenizer







						def __init__(	self :   Dict					,     lowerCAmelCase__ :   Any=None					,     lowerCAmelCase__ :   List[Any]=None					,     lowerCAmelCase__ :   List[str]=None					,     lowerCAmelCase__ :   str="replace"					,     lowerCAmelCase__ :   Optional[int]="<s>"					,     lowerCAmelCase__ :   Optional[Any]="</s>"					,     lowerCAmelCase__ :   Optional[Any]="</s>"					,     lowerCAmelCase__ :   Any="<s>"					,     lowerCAmelCase__ :   List[Any]="<unk>"					,     lowerCAmelCase__ :   Optional[Any]="<pad>"					,     lowerCAmelCase__ :   Optional[Any]="<mask>"					,     lowerCAmelCase__ :   Dict=False					,     lowerCAmelCase__ :   Union[str, Any]=True					,     **lowerCAmelCase__ :   str					,     ):

													"""simple docstring"""
													super().__init__(
													    lowerCAmelCase__					,     lowerCAmelCase__					,     tokenizer_file=lowerCAmelCase__					,     errors=lowerCAmelCase__					,     bos_token=lowerCAmelCase__					,     eos_token=lowerCAmelCase__					,     sep_token=lowerCAmelCase__					,     cls_token=lowerCAmelCase__					,     unk_token=lowerCAmelCase__					,     pad_token=lowerCAmelCase__					,     mask_token=lowerCAmelCase__					,     add_prefix_space=lowerCAmelCase__					,     trim_offsets=lowerCAmelCase__					,     **lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   List[Any]         =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
													if pre_tok_state.get('''add_prefix_space'''					,     lowerCAmelCase__					) != add_prefix_space:
																				SCREAMING_SNAKE_CASE       :   Any         =      getattr(lowerCAmelCase__					,     pre_tok_state.pop('''type'''					)					)
																				SCREAMING_SNAKE_CASE       :   str         =      add_prefix_space
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      pre_tok_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Any         =      add_prefix_space

													# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      '''post_processor'''
													SCREAMING_SNAKE_CASE       :   Dict         =      getattr(self.backend_tokenizer					,     lowerCAmelCase__					,     lowerCAmelCase__					)
													if tokenizer_component_instance:
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      json.loads(tokenizer_component_instance.__getstate__()					)

																				# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																				if "sep" in state:
																											SCREAMING_SNAKE_CASE       :   Dict         =      tuple(state['''sep''']					)
																				if "cls" in state:
																											SCREAMING_SNAKE_CASE       :   int         =      tuple(state['''cls''']					)

																				SCREAMING_SNAKE_CASE       :   int         =      False

																				if state.get('''add_prefix_space'''					,     lowerCAmelCase__					) != add_prefix_space:
																											SCREAMING_SNAKE_CASE       :   Any         =      add_prefix_space
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      True

																				if state.get('''trim_offsets'''					,     lowerCAmelCase__					) != trim_offsets:
																											SCREAMING_SNAKE_CASE       :   Dict         =      trim_offsets
																											SCREAMING_SNAKE_CASE       :   Any         =      True

																				if changes_to_apply:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      getattr(lowerCAmelCase__					,     state.pop('''type'''					)					)
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      component_class(**lowerCAmelCase__					)
																											setattr(self.backend_tokenizer					,     lowerCAmelCase__					,     lowerCAmelCase__					)







						@property
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													if self._mask_token is None:
																				if self.verbose:
																											logger.error('''Using mask_token, but it is not set yet.'''					)
																				return None
													return str(self._mask_token					)







						@mask_token.setter
						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      AddedToken(lowerCAmelCase__					,     lstrip=lowerCAmelCase__					,     rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					) else value
													SCREAMING_SNAKE_CASE       :   int         =      value







						def        __lowercase     (	self :   List[str]					,     *lowerCAmelCase__ :   List[Any]					,     **lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      kwargs.get('''is_split_into_words'''					,     lowerCAmelCase__					)

													if is_split_into_words and not self.add_prefix_space:
																				raise ValueError(
																				    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																				    '''to use it with pretokenized inputs.'''					)

													return super()._batch_encode_plus(*lowerCAmelCase__					,     **lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					,     *lowerCAmelCase__ :   Optional[Any]					,     **lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      kwargs.get('''is_split_into_words'''					,     lowerCAmelCase__					)

													if is_split_into_words and not self.add_prefix_space:
																				raise ValueError(
																				    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																				    '''to use it with pretokenized inputs.'''					)

													return super()._encode_plus(*lowerCAmelCase__					,     **lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Optional[str] = None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self._tokenizer.model.save(lowerCAmelCase__					,     name=lowerCAmelCase__					)
													return tuple(lowerCAmelCase__					)







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   Optional[int]=None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
													if token_ids_a is None:
																				return output

													return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   List[int]					,     lowerCAmelCase__ :   Optional[List[int]] = None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      [self.sep_token_id]
													SCREAMING_SNAKE_CASE       :   Tuple         =      [self.cls_token_id]

													if token_ids_a is None:
																				return len(cls + token_ids_a + sep					) * [0]
													return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]









'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					Optional[int]       ,		A	:					int					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   str         =      TaConfig.from_json_file(A					)
							print(F"""Building PyTorch model from configuration: {config}"""					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TaForConditionalGeneration(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(A       ,		A       ,		A					)

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Union[str, Any]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Tuple             =    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




